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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

共 68658 条记录，以下是4741-4750 订阅

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Chinese Named Entity Recognition Based on Grid Tagging and Semantic Segmentation 9

Chinese Named Entity Recognition Based on Grid Tagging and S...

引用

9th IEEE international conference on Cloud Computing and Intelligence Systems, CCIS 2023

作者： Zhao, Xuedong Shi, Zhiliang Xiang, Yan Ren, Ying Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming650500 China Kunming Enersun Technology Co. Ltd. Kunming650217 China

ISBN: (纸本)9798350304428

Chinese Named Entity Recognition (CNER) is one of the fundamental tasks in Chinese Natural Language Processing and has been extensively researched. However, previous studies have primarily focused on specific types of CNER subtasks, which hindered the overall progress and practical implementation of CNER. Recently, grid-tagging-based methods have emerged as having significant advantages in information extraction tasks due to the flexible annotation framework and model architecture they provide. Building on this, we unify the CNER task as a character pair relation classification problem and solve it by predicting the character pair relation matrix, which is similar to the semantic segmentation task in computer vision. To do this, we utilize the U-shaped segmentation module to capture low-level and high-level features in the feature map of image style, enabling us to extract more semantic information. We also effectively model the boundary information of the entity, which in turn enhances the overall accuracy of entity recognition. To test the effectiveness of our method, we conduct experiments on two NER datasets containing flat and nested entities in the Chinese medical domain (CMeEEV2, MMC). Our results demonstrate that our approach improves the F1 score by 0.73% and 1.07% respectively compared to current state-of-the-art models, further validating its effectiveness. © 2023 IEEE.

关键词： Semantic Segmentation

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Natural Gas Price Forecasting using Statistical Models and Deep Learning Models 2

Natural Gas Price Forecasting using Statistical Models and D...

引用

2nd international conference on Advancements in Electrical, Electronics, Communication, Computing and automation, ICAECA 2023

作者： Murugan, Devasenan Shivaramakrishnan, Rajendiran Bharathi, Priya C. Yamunathangam, D. Kumaraguru College of Technology Computer Science Engineering Coimbatore India

ISBN: (纸本)9798350306811

Natural gas, entitled as methane gas or natural methane gas, is a highly flammable, colorless, odorless gaseous hydrocarbon where ethane and methane form the core. A petroleum resource which is associated with crude oil, burning it results in less emission of carbon which promotes a sustainable environment. In order to be extremely safe, go eco green, reduce dependency on nations for fuel resources it is even more tactical to forecast the prices of natural gases in the international market for a time frame. The present models shine either on statistical or deep learning models making it a. Thus, an ARIMA model is developed initially by using the updated dataset(Nasdaq) for the forecast to predict the closing price of the day. The autoregression predicts the upcoming values(closing price) based on then values. Moving Averages play a crucial role in smoothing the time series data. Secondly, the LSTM model is constructed with the same data frame. LSTM uses recurrent neural networks (RNN). The ideology behind the model is that at times being conscious of recent information to perform the present task. Bidirectional LSTM is also constructed. The Neural Prophet which is built on the top of pytorch is also experimented by means of forecasting. Neural prophet is extensively used by developers for the extension of the framework. The experimental repercussion showed that the proposed models are more efficient in terms of prediction and accuracy of the closing price © 2023 IEEE.

关键词： Natural gas

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Auto Tuning Quantized Graph Neural Networks on GPU Tensor Core via TVM 5

Auto Tuning Quantized Graph Neural Networks on GPU Tensor Co...

引用

5th international conference on Frontiers technology of Information and computer, ICFTIC 2023

作者： Wang, Zhiyuan University of Science and Technology of China School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350309034

Research on quantized graph neural networks (QGNN) have driven the development of related inference acceleration techniques. Both lower memory footprint and lower inference latency have led more and more researchers to focus on these new techniques. Although there is work on how to boost the performance of QGNN on modern GPUs by significant manual effort, how to automate building high-performance kernels on modern GPUs is still our concern. We propose AQGN, an extension plug-in on the TVM compilation stack for automatically exploring the sparsity of quantized graph neural networks on new tensor core hardware. Combined with post training quantization method, AQGN does not require model retraining. We construct a extended configuration search space to automate model tuning. We craft a module in TVM that automatically search the quantization configuration space given the hardware's feedback. Experimental data demonstrates that our implementation outperforms the current DGL framework in terms of efficiency. © 2023 IEEE.

关键词： Edge Computing High-performance Computing Quantized Graph Neural Networks

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Finger Gesture Recognition using Recurrent Neural Network and Convolution Neural Network 7

Finger Gesture Recognition using Recurrent Neural Network an...

引用

2023 7th international conference On Computing, Communication, Control And automation, ICCUBEA 2023

作者： Kizhekethottam, Priyanka Sherekar, Amisha Thakur, Pradnya Takale, Bhagyashree Pathan, Mehzabin Pimpri Chinchwad College of Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9798350304268

Communication is essential for people to interact with one another. Sadly, not everyone can communicate verbally, so they resort to Sign language to engage in conversations and express themselves using various hand gestures. The deaf community mostly uses American sign language (ASL). However, only a few people can interpret ASL, and various translation systems have been developed. We aim to implement various Deep Learning techniques to translate ASL and read the sign out loud to find and develop an efficient and accurate sign language translation to make communication more accessible for hard-of-hearing individuals by building Recurrent Neural Networks and Convolution Neural Networks. On evaluating both these models highlighting the strengths and weaknesses of each approach. This is conducted based on various hyperparameters such as the Number of Epochs, Accuracy, Time, and Space Complexity. The study's findings offer exciting prospects for improving hand gesture recognition technology. © 2023 IEEE.

关键词： Accuracy American Sign Language computer Vision Convolutional Neural Networks Efficiency Hand Gesture Recognition Image Analysis LSTM Mediapipe Performance Evaluation Recurrent Neural Networks Sign Language Translation Tensorflow Time Module

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Weapon Detection from Surveillance Footage in Real-Time Using Deep Learning 7

Weapon Detection from Surveillance Footage in Real-Time Usin...

引用

2023 7th international conference On Computing, Communication, Control And automation, ICCUBEA 2023

作者： Jadhav, Vedantika Deshmukh, Rutuja Gupta, Palak Ghogale, Sharvi Bodireddy, Mahalakshmi Pimpri Chinchwad College of Engineering Computer Department Pune India

ISBN: (纸本)9798350304268

Weapon violence is a serious threat nowadays due to the rise in technology and criminal intelligence. Manual surveillance is a very tedious task because these activities are atypical in comparison with everyday activities. By introducing machine learning techniques to detect such activities, we can minimise the risk of human errors and prevent details from going unnoticed. Current systems fail when it comes to efficiency of the system and ease of use. We have implemented various models like CNN, YOLOV7, YOLOV8 and VGG for weapon detection and identified their strengths and limitations. A complete system for detection of weapons using these models and raising an alarm has been implemented here to overcome the problems of manual surveillance. Results are presented in a table focusing on metrics like precision and recall as these metrics prove to be more insightful and reliable as compared to accuracy for object detection. © 2023 IEEE.

关键词： AATpI CNN deep learning gun detection object detection YOLO

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Personality Prediction Using ML and NLP 7

Personality Prediction Using ML and NLP

引用

2023 7th international conference On Computing, Communication, Control And automation, ICCUBEA 2023

作者： Mahandule, Adinath Fiske, Gaurav Malusare, Vedang Bhambure, Omkar Shedge, Rajashree Ramrao Adik Institute of Technology Dept. of Computer Engineering Navi Mumbai400706 India

ISBN: (纸本)9798350304268

Estimating a person's personality is essential in understanding how they manage stress, lead, communicate, collaborate, and influence others. One of the critical factors that affect how people interact with their environment is their personality. This project aims to utilize machine learning and also natural language processing techniques to predict the personality of students based on a questionnaire. The system analyzes the user's personality in the database, which already records personality traits. The system then identifies the user's personality based on the MBTI model, a widely used personality model. This project's significance lies in the capability of the system to aid students, teachers, and businesses in identifying students' personalities to plan their actions better. The ML and NLP techniques implemented in this system can categorize or anticipate students' personalities, thus assisting businesses in their hiring process. © 2023 IEEE.

关键词： Catboost Gradient Boosting Machine Learning Myers-Briggs Type Indicator (MBTI) Natural Language Processing TF-IDF(Term Frequency-Inverse Document Frequency)

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Non Small Cell Lung Cancer Diagnosis Using kNN and Logistic Regression 14

Non Small Cell Lung Cancer Diagnosis Using kNN and Logistic ...

引用

14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Siddique Ibrahim, S.P. Bharathi Priya, C. Selva Kumar, S. Vinoth Kumar, S. VIT-AP University School of Computer Science and Engineering Andhra Pradesh Amaravati India Kumaraguru College of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Dept of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350335095

One in nine people develop cancer during their life time in India. Chances are that we already know someone who has been affected by cancer. But what is cancer? Simply put, they are made of trillions of cells that grow and divide over one's lifetime as needed. Abnormal or old cells die after a certain period of time but some cells do not die and overcrowd the space and stop the body from functioning normally. The most recent screening technology makes early identification of lung cancer simple and may extend the patient's life. The use of automated technologies can also improve the accuracy of illness detection, aiding medical professionals in making accurate diagnoses. The automated lung cancer diagnosis method described in this article uses a machine learning algorithm to distinguish between benign, malignant, and normal lung cancer. The proposed lung cancer detection technique has a 98.7% accuracy rate, which is higher than the approaches that were examined. © 2023 IEEE.

关键词： Cytology

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Pneumonia Detection from X-ray Images Using Convolutional Neural Networks 24

Pneumonia Detection from X-ray Images Using Convolutional Ne...

引用

24th international conference on Control Systems and computer science, CSCS 2023

作者： Dediu, Viorel Udrea, Andreea Faculty of Automatic Control and Computer Science Univesity Politehnica of Bucharest Department of Automatic Control and Systems Engineering Bucharest Romania

ISBN: (纸本)9798350313390

Pneumonia is one of the top causes of death in Romania and early detection of this disease improves the recovery chances and shortens the length of hospitalization. In this work, we develop a solution for automatic pneumonia detection based on convolutional neural networks. Four network models are investigated. They are trained on 4.163 images from a public dataset and tested on 530 images. The best results are obtained by one of the proposed models conducting to a sensitivity of 98.72%, an accuracy of 89.81%, and ROC 93.46%. Thus, this research proposes a lightweight screening tool that can help triaging the patients with pneumonia. © 2023 IEEE.

关键词： Convolution

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Auxiliary Decision-Making Strategy for Physical Quadrotor by Actively Interacting with Virtual System 8

Auxiliary Decision-Making Strategy for Physical Quadrotor by...

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8th international conference on automation, Control and Robotics Engineering, CACRE 2023

作者： Yang, Yuanlin Meng, Wei University of Technology School of Automation Guangdong Guangzhou China

ISBN: (纸本)9798350302776

With the help of 5G network technology, the physical system (PS) can communicate with its virtual system (VS) in real-time. This high-speed communication is particularly advantageous for robots such as Quadrotors which operate over large distances. Digital twin (DT) technology concerns the relationship between PS and VS. In the VS, simulation technology allows states of virtual objects to be fully captured, and further input schemes can be designed to simulate additional states. Therefore, simulation results are utilized to enhance the decision-making capabilities of physical objects. In this paper, the VS serves as an online decision-making center, providing auxiliary decision-making services to physical objects taking into account of communication delays during the interaction process between PS and VS. Two types of experiments are conducted to test the proposed auxiliary decision-making strategy with VS participation. Using the Quadrotor as a physical object, two type experiments are conducted to verify the proposed strategy, including virtual simulation experiment and physical machine testing. The experimental results demonstrate that the Quadrotor in PS can successfully accomplish its mission under the guidance of the VS. Moreover, successful outdoor testing of the physical machine with 5G network has been conducted. © 2023 IEEE.

关键词： Decision making

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An Intrusion Detection System Empowered by Deep Learning Algorithms

An Intrusion Detection System Empowered by Deep Learning Alg...

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2023 IEEE international conference on Dependable, Autonomic and Secure Computing, 2023 international conference on Pervasive Intelligence and Computing, 2023 international conference on Cloud and Big Data Computing, 2023 international conference on Cyber science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Tang, Weiye Li, Daren Fan, Wuyue Liu, Tianhao Chen, Man Dib, Omar Wenzhou-kean University Department of Computer Science Wenzhou China

ISBN: (纸本)9798350304602

The Industrial Internet of Things (IIoT) faces significant challenges in managing the increasing volume of network traffic, which calls for robust cybersecurity measures, especially in intrusion detection. An effective intrusion detection system (IDS) is essential to protect industrial systems from cyber attacks, enabling quick identification and response to security threats. However, traditional machine learning techniques have limitations in capturing complex patterns and correlations in the extensive data generated by IoT devices. Deep learning (DL) offers a promising alternative by employing multi-layer neural networks to extract hierarchical abstract features, resulting in improved accuracy and generalization. DL models handle large and complex datasets and address intricate problems, making them ideal for overcoming security challenges in cybersecurity domains. In this context, this paper explores the application of recent DL and optimization techniques in IIoT intrusion detection to develop more accurate, robust, and efficient models for threat detection. Experimental results on the recent EdgeIIoT dataset demonstrate a remarkable accuracy rate, and rapid responsiveness within our framework, effectively identifying various known attacks and affirming the proposed IDS system's feasibility. © 2023 IEEE.

关键词： Cybersecurity

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